Identifying Customer Churn

As you are working your “High Value” customers, you often lose sight of the smaller customers, they often just “fall off the radar” and you do not know why. These small customers can add up to substantial amounts. Customer churn analysis identifies the health of your customer base across multiple dimensions to create a better view of customers at risk of leaving your business.

BI reporting provides the “top ten” customers ranked by sales, which is a good initial indicator, but does not provide a holistic view into your customer base. The objective of utilizing a Churn Report utilizing Predictive Analytics is to understand trends across the dimensions before they become a critical event; losing a customer.

Churn Analysis with SAP Predictive Analytics

Churn analysis is the systematic analysis of sales history across multiple dimensions including, but not limited to:

Recency – When was the last time a customer placed an order?

Frequency – How frequently does a customer place orders?

Monetary – What is the customer spend in dollars?

Profitability – What is the customer profitability in dollars?

Profitability % – What is the overall customer profitability percent?

Longevity – Time duration they have been a customer

The output provides a relative ranking by customer for each dimension and an overall customer ranking. You can then use the output to stratify your customers into value to your business. Those with the highest scores warrant a different sales strategy then those with the lowest values. Churn analysis focuses on customers with the lowest values.

The Solution

It is expensive to obtain a customer and without attention, they will find alternatives. Churn analysis using SAP Predictive Analytics keeps you in touch with your client base. Knowing which customers are at risk for churn gives you a clearer picture of where to focus your attention, or if attention is worth focusing on low value customers.

One Predictive Analytics user summed it up by saying “This analysis is our special sauce”. If you’d like to see a demo of SAP Predictive Analytics in use, view our on demand recording of our Predictive Analytics Webinar that covers customer churn as well as how to build a forecast using internal and external data.

Author:
Robert Chicvak

Bob Chicvak utilizes predictive analytics to optimize the complete supply chain. His work includes: inventory reduction while increasing customer service levels, customers at risk of churn, SKU rationalization, promotion effectiveness, product mix and predictive maintenance. A recent project at a CPG client reduced inventory 22% while increasing the order fill rate up to 95%. Another site identified the top 50 customers at risk of churn and identified the most effective promotions, saving over $1.1 M. Using market basket analysis he developed a model to optimize the product mix for promotions. He has implemented business optimization systems in distribution and manufacturing companies over the last 30 years.